Unofficial news and tips about Google

October 8, 2008

Machine Translation and Speech Recognition at Google

Google's technologies for automatic translation and speech recognition already have visible results: you can translate texts in 35 languages at translate.google.com or use voice to find a local business with GOOG-411, but Google intends to expand their use. You should be able to translate an email written in a foreign language or find answers to simple questions by voice.

Mike Cohen, who leads Google's speech technology efforts, and Franz Och, machine translation researcher, chat with Alfred Spector, VP of Research and Special Initiatives at Google, about two technologies that might seem unrelated to Google's core competency. Both statistical machine translation and speech recognition are search problems and Google's computer infrastructure can process large amounts of data that are needed to build language models. Another big advantage for Google is that it has popular services that generate a lot of useful data.

"When we first created GOOG-411, we had no speech data. Because we had so much query data here at Google (textual queries that people had typed to Google Maps), we could already train a pretty good language model. Now, obviously, text is a little different than speech and now that we've also trained on speech, we have better performance than we had back then, but even out of the box we could get good performance on that problem because we had so much textual data," says Mike Cohen.

17 comments:

That's excellent -- now if we could just get some of that voice recognition technology applied to Grand Central... It would be about 200% more useful to me if I could "say" one, two, three, etc. rather than having to hit a key during call screening...

Completely agree Chuck. It would be nice to get voice mails as text too. It seems that all of these technologies have enormous potential for GrandCentral but there seems to be no GC focus what so ever.

The view that "[] ...speech recognition [is] search problem[s]" is common, but I think there are better ways to capture/identify speech. Translation is a different issue and mass data analysis may be appropriate here.

I will third that comment by Chuck. My phone doesn't let you input numbers when you've received a call and it's in password lock, so if my phone happens to be password locked (it automatically locks every few hours) and I get a call, I can't answer and I'm forced to wait until the party inevitably hangs up. For this reason, I don't give out my GC number unless I'm sure someone's going to try to bug me. I'm annoyed at how long it's taking them to get GrandCentral up to speed, but I can only assume they must be planning a big overhaul soon, especially considering their Android platform is rolling out.

@steeleweed: "mass data analysis" is part of what happens when doing speech recognition as a search problem. Search is also applicable for parsing syntactic structure, and is thus an important ingredient in many machine translations systems. Besides, most of the NLP technology out there uses a rather eclectic mix of methods...

I would like to see Google offer a speech recognition service that allows you to record a long audio message into your smart phone and have it transcribed into text and posted on your blog. This service would be useful to writers and reporters because they wouldn't have to worry about losing a thought in the process of scrambling to write it down.

This service could aid Google in their speech recognition and language translation effort in the following way: In exchange for the free service outlined above Google would periodically send each user a paragraph to read out loud for submission. In addition, since Google has such deep pockets, they could offer a lottery to solicit even more people to use this service.

So basically, a smart phone application would record your speech and upload it to Google's servers when you get close to a hot spot. The speech could then get transcribed asynchronously into text and posted on your blog. No need to transcribe it in real time since accuracy is the goal for everyone.

Since everybody has a different voice, voice recognition (and live interpretation) seems much more difficult than just machine translation (of text). We already know there is much work to be done for machine translation. So I don't expect voice recognition to progress to the point live machine interpretation is feasible. That would also probably require quite a lot of training from the users. If you have ever tried a voice recognition program on your PC to replace your keyboard you should know...Anyway, we have to start somewhere, and any initiative to improve machine translation and/or voice recognition should be welcomed.